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. 2011a. Biplot analysis of diallel crosses of early maturing tropical yellow maize inbreds in stress and nonstress environments. Crop Sci. 51 :173–188. Oyekunle M. Biplot analysis of

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. Rubio , J. , Cubero , J.I. , Martin , L.M. , Suso , M.J. , Flores , F. 2004 . Biplot analysis of trait relations of white lupin in Spain . Euphytica 135 : 217 – 224

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Dehghani, H., Ebadi, A., Yousefi, A. (2006): Bi-plot analysis of genotype by environment interaction for barley yield in Iran. Agron. J., 98, 388–393. Yousefi A. Bi-plot analysis of

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References Akinwale , R.O. , Fakorede , M.A.B. , Badu-Apraku , B. , Oluwaranti , A. 2014 . Assessing the usefulness of GGE Biplot as a statistical tool for

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Yan, W. 2001. GGE biplot — a Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron. J. 93 :1111–1118. Yan W. GGE biplot

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, Q., Szlavnics, Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40 : 597–605. Szlavnics Z. Cultivar evaluation and mega

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Cereal Research Communications
Authors: N. Pržulj, M. Mirosavljević, P. Čanak, M. Zorić and J. Boćanski

. Lipkovich , I. , Smith , E.P. 2002 . Biplot and singular value decomposition macros for Excel . J. Stat. Softw. 7 : 1 – 15 . Metzger , M.J. , Bunce , R.G.H. , Jongman

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. Solonechnyi , P. , Vasko , N. , Naumov , A. , Solonechnaya , O. , Vazhenina , O. , Bondareva , O. , Logvinenko , Y. 2015 . GGE biplot analysis of genotype by environment interaction of spring barley varieties . Zemdirbyste 102 : 431

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Cereal Research Communications
Authors: B. Vaezi, A. Pour-Aboughadareh, R. Mohammadi, M. Armion, A. Mehraban, T. Hossein-Pour and M. Dorii

Successful production and development of stable and adaptable cultivars only depend on the positive results achieved from the interaction between genotype and environment that consequently has significant effect on breeding strategies. The objectives of this study were to evaluate genotype by environment interactions for grain yield in barley advanced lines and to determine their stability and general adaptability. For these purposes, 18 advanced lines along with two local cultivars were evaluated at five locations (Gachsaran, Lorestan, Ilam, Moghan and Gonbad) during three consecutive years (2012–2015). The results of the AMMI analysis indicated that main effects due to genotype (G), environment (E) and GE interaction as well as four interaction principal component axes were significant, representing differential responses of the lines to the environments and the need for stability analysis. According to AMMI stability parameters, lines G5 and G7 were the most stable lines across environments. Biplot analysis determined two barley mega-environments in Iran. The first mega-environment contained of Ilam and Gonbad locations, where the recommended G13, G19 and G1 produced the highest yields. The second mega-environment comprised of Lorestan, Gachsarn and Moghan locations, where G2, G9, G5 and G7 were the best adapted lines. Our results revealed that lines G5, G7, G9 and G17 are suggested for further inclusion in the breeding program due to its high grain yield, and among them G5 recommended as the most stable lines for variable semi-warm and warm environments. In addition, our results indicated the efficiency of AMMI and GGE biplot techniques for selecting genotypes that are stable, high yielding, and responsive.

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The genotype by environment (GE) interaction is a major problem in the study of quantitative traits because it complicates the interpretation of genetic experiments and makes predictions difficult. In order to quantify GE interaction effects on the grain yield of durum wheat and to determine stable genotypes, field experiments were conducted with ten genotypes for four consecutive years in two different conditions (irrigated and rainfed) in a completely randomized block design with three replications in each environment. Combined analysis of variance exhibited significant differences for the GE interaction, indicating the possibility of stable entries. The results of additive main effect and multiplicative interaction (AMMI) analysis revealed that 12% of total variability was justified by the GE interaction, which was six times more than that of genotype. Ordination techniques displayed high differences for the interaction principal components (IPC1, IPC2 and IPC3), indicating that 92.5% of the GE sum of squares was justified by AMMI1, AMMI2 and AMMI3, i.e. 4.5 times more than that explained by the linear regression model. The results of the AMMI model and biplot analysis showed two stable genotypes with high grain yield, due to general adaptability to both rainfed and irrigated conditions, and one with specific adaptation.

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